SSVEP-Based Brain Computer Interface Controlled Soft Robotic Glove for Post-Stroke Hand Function Rehabilitation Article Swipe
Ning Guo
,
Xiaojun Wang
,
Dehao Duanmu
,
Xin Huang
,
Xiaodong Li
,
Yunli Fan
,
Hailan Li
,
Yongquan Liu
,
Eric Yeung
,
Michael To
,
GU Jian-xiong
,
Feng Wan
,
Yong Hu
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/tnsre.2022.3185262
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/tnsre.2022.3185262
Soft robotic glove with brain computer interfaces (BCI) control has been used for post-stroke hand function rehabilitation. Motor imagery (MI) based BCI with robotic aided devices has been demonstrated as an effective neural rehabilitation tool to improve post-stroke hand function. It is necessary for a user of MI-BCI to receive a long time training, while the user usually suffers unsuccessful and unsatisfying results in the beginning. To propose another non-invasive BCI paradigm rather than MI-BCI, steady-state visually evoked potentials (SSVEP) based BCI was proposed as user intension detection to trigger the soft robotic glove for post-stroke hand function rehabilitation. Thirty post-stroke patients with impaired hand function were randomly and equally divided into three groups to receive conventional, robotic, and BCI-robotic therapy in this randomized control trial (RCT). Clinical assessment of Fugl-Meyer Motor Assessment of Upper Limb (FMA-UL), Wolf Motor Function Test (WMFT) and Modified Ashworth Scale (MAS) were performed at pre-training, post-training and three months follow-up. In comparing to other groups, The BCI-robotic group showed significant improvement after training in FMA full score (10.05 ± 8.03, p = 0.001), FMA shoulder/elbow (6.2 ± 5.94, p = 0.0004) and FMA wrist/hand (4.3 ± 2.83, p = 0.007), and WMFT (5.1 ± 5.53, p = 0.037). The improvement of FMA was significantly correlated with BCI accuracy (r = 0.714, p = 0.032). Recovery of hand function after rehabilitation of SSVEP-BCI controlled soft robotic glove showed better result than solely robotic glove rehabilitation, equivalent efficacy as results from previous reported MI-BCI robotic hand rehabilitation. It proved the feasibility of SSVEP-BCI controlled soft robotic glove in post-stroke hand function rehabilitation.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tnsre.2022.3185262
- https://ieeexplore.ieee.org/ielx7/7333/4359219/09803244.pdf
- OA Status
- diamond
- Cited By
- 84
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4283318593
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4283318593Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/tnsre.2022.3185262Digital Object Identifier
- Title
-
SSVEP-Based Brain Computer Interface Controlled Soft Robotic Glove for Post-Stroke Hand Function RehabilitationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-01-01Full publication date if available
- Authors
-
Ning Guo, Xiaojun Wang, Dehao Duanmu, Xin Huang, Xiaodong Li, Yunli Fan, Hailan Li, Yongquan Liu, Eric Yeung, Michael To, GU Jian-xiong, Feng Wan, Yong HuList of authors in order
- Landing page
-
https://doi.org/10.1109/tnsre.2022.3185262Publisher landing page
- PDF URL
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https://ieeexplore.ieee.org/ielx7/7333/4359219/09803244.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://ieeexplore.ieee.org/ielx7/7333/4359219/09803244.pdfDirect OA link when available
- Concepts
-
Brain–computer interface, Rehabilitation, Physical medicine and rehabilitation, Stroke (engine), Motor function, Motor imagery, Medicine, Physical therapy, Electroencephalography, Engineering, Mechanical engineering, PsychiatryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
84Total citation count in OpenAlex
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2025: 28, 2024: 33, 2023: 21, 2022: 2Per-year citation counts (last 5 years)
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35Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.is | 41 |
| abstract_inverted_index.of | 46, 129, 133, 206, 221, 226, 255 |
| abstract_inverted_index.to | 35, 48, 88, 114, 158 |
| abstract_inverted_index.± | 174, 182, 191, 199 |
| abstract_inverted_index.BCI | 21, 70, 81, 212 |
| abstract_inverted_index.FMA | 170, 179, 188, 207 |
| abstract_inverted_index.The | 161, 204 |
| abstract_inverted_index.and | 60, 108, 118, 142, 152, 187, 196 |
| abstract_inverted_index.for | 12, 43, 94 |
| abstract_inverted_index.has | 9, 26 |
| abstract_inverted_index.the | 55, 64, 90, 253 |
| abstract_inverted_index.was | 82, 208 |
| abstract_inverted_index.(4.3 | 190 |
| abstract_inverted_index.(5.1 | 198 |
| abstract_inverted_index.(6.2 | 181 |
| abstract_inverted_index.(MI) | 19 |
| abstract_inverted_index.Limb | 135 |
| abstract_inverted_index.Soft | 0 |
| abstract_inverted_index.Test | 140 |
| abstract_inverted_index.WMFT | 197 |
| abstract_inverted_index.Wolf | 137 |
| abstract_inverted_index.been | 10, 27 |
| abstract_inverted_index.from | 244 |
| abstract_inverted_index.full | 171 |
| abstract_inverted_index.hand | 14, 38, 96, 104, 222, 249, 263 |
| abstract_inverted_index.into | 111 |
| abstract_inverted_index.long | 51 |
| abstract_inverted_index.soft | 91, 229, 258 |
| abstract_inverted_index.than | 73, 235 |
| abstract_inverted_index.this | 122 |
| abstract_inverted_index.time | 52 |
| abstract_inverted_index.tool | 34 |
| abstract_inverted_index.used | 11 |
| abstract_inverted_index.user | 45, 56, 85 |
| abstract_inverted_index.were | 106, 147 |
| abstract_inverted_index.with | 3, 22, 102, 211 |
| abstract_inverted_index.(BCI) | 7 |
| abstract_inverted_index.(MAS) | 146 |
| abstract_inverted_index.2.83, | 192 |
| abstract_inverted_index.5.53, | 200 |
| abstract_inverted_index.5.94, | 183 |
| abstract_inverted_index.8.03, | 175 |
| abstract_inverted_index.Motor | 17, 131, 138 |
| abstract_inverted_index.Scale | 145 |
| abstract_inverted_index.Upper | 134 |
| abstract_inverted_index.after | 167, 224 |
| abstract_inverted_index.aided | 24 |
| abstract_inverted_index.based | 20, 80 |
| abstract_inverted_index.brain | 4 |
| abstract_inverted_index.glove | 2, 93, 231, 238, 260 |
| abstract_inverted_index.group | 163 |
| abstract_inverted_index.other | 159 |
| abstract_inverted_index.score | 172 |
| abstract_inverted_index.three | 112, 153 |
| abstract_inverted_index.trial | 125 |
| abstract_inverted_index.while | 54 |
| abstract_inverted_index.(10.05 | 173 |
| abstract_inverted_index.(RCT). | 126 |
| abstract_inverted_index.(WMFT) | 141 |
| abstract_inverted_index.0.714, | 216 |
| abstract_inverted_index.MI-BCI | 47, 247 |
| abstract_inverted_index.Thirty | 99 |
| abstract_inverted_index.better | 233 |
| abstract_inverted_index.evoked | 77 |
| abstract_inverted_index.groups | 113 |
| abstract_inverted_index.months | 154 |
| abstract_inverted_index.neural | 32 |
| abstract_inverted_index.proved | 252 |
| abstract_inverted_index.rather | 72 |
| abstract_inverted_index.result | 234 |
| abstract_inverted_index.showed | 164, 232 |
| abstract_inverted_index.solely | 236 |
| abstract_inverted_index.(SSVEP) | 79 |
| abstract_inverted_index.0.0004) | 186 |
| abstract_inverted_index.0.001), | 178 |
| abstract_inverted_index.0.007), | 195 |
| abstract_inverted_index.0.032). | 219 |
| abstract_inverted_index.0.037). | 203 |
| abstract_inverted_index.MI-BCI, | 74 |
| abstract_inverted_index.another | 68 |
| abstract_inverted_index.control | 8, 124 |
| abstract_inverted_index.devices | 25 |
| abstract_inverted_index.divided | 110 |
| abstract_inverted_index.equally | 109 |
| abstract_inverted_index.groups, | 160 |
| abstract_inverted_index.imagery | 18 |
| abstract_inverted_index.improve | 36 |
| abstract_inverted_index.propose | 67 |
| abstract_inverted_index.receive | 49, 115 |
| abstract_inverted_index.results | 62, 243 |
| abstract_inverted_index.robotic | 1, 23, 92, 230, 237, 248, 259 |
| abstract_inverted_index.suffers | 58 |
| abstract_inverted_index.therapy | 120 |
| abstract_inverted_index.trigger | 89 |
| abstract_inverted_index.usually | 57 |
| abstract_inverted_index.Ashworth | 144 |
| abstract_inverted_index.Clinical | 127 |
| abstract_inverted_index.Function | 139 |
| abstract_inverted_index.Modified | 143 |
| abstract_inverted_index.Recovery | 220 |
| abstract_inverted_index.accuracy | 213 |
| abstract_inverted_index.computer | 5 |
| abstract_inverted_index.efficacy | 241 |
| abstract_inverted_index.function | 15, 97, 105, 223, 264 |
| abstract_inverted_index.impaired | 103 |
| abstract_inverted_index.paradigm | 71 |
| abstract_inverted_index.patients | 101 |
| abstract_inverted_index.previous | 245 |
| abstract_inverted_index.proposed | 83 |
| abstract_inverted_index.randomly | 107 |
| abstract_inverted_index.reported | 246 |
| abstract_inverted_index.robotic, | 117 |
| abstract_inverted_index.training | 168 |
| abstract_inverted_index.visually | 76 |
| abstract_inverted_index.(FMA-UL), | 136 |
| abstract_inverted_index.SSVEP-BCI | 227, 256 |
| abstract_inverted_index.comparing | 157 |
| abstract_inverted_index.detection | 87 |
| abstract_inverted_index.effective | 31 |
| abstract_inverted_index.function. | 39 |
| abstract_inverted_index.intension | 86 |
| abstract_inverted_index.necessary | 42 |
| abstract_inverted_index.performed | 148 |
| abstract_inverted_index.training, | 53 |
| abstract_inverted_index.Assessment | 132 |
| abstract_inverted_index.Fugl-Meyer | 130 |
| abstract_inverted_index.assessment | 128 |
| abstract_inverted_index.beginning. | 65 |
| abstract_inverted_index.controlled | 228, 257 |
| abstract_inverted_index.correlated | 210 |
| abstract_inverted_index.equivalent | 240 |
| abstract_inverted_index.follow-up. | 155 |
| abstract_inverted_index.interfaces | 6 |
| abstract_inverted_index.potentials | 78 |
| abstract_inverted_index.randomized | 123 |
| abstract_inverted_index.wrist/hand | 189 |
| abstract_inverted_index.BCI-robotic | 119, 162 |
| abstract_inverted_index.feasibility | 254 |
| abstract_inverted_index.improvement | 166, 205 |
| abstract_inverted_index.post-stroke | 13, 37, 95, 100, 262 |
| abstract_inverted_index.significant | 165 |
| abstract_inverted_index.demonstrated | 28 |
| abstract_inverted_index.non-invasive | 69 |
| abstract_inverted_index.steady-state | 75 |
| abstract_inverted_index.unsatisfying | 61 |
| abstract_inverted_index.unsuccessful | 59 |
| abstract_inverted_index.conventional, | 116 |
| abstract_inverted_index.post-training | 151 |
| abstract_inverted_index.pre-training, | 150 |
| abstract_inverted_index.significantly | 209 |
| abstract_inverted_index.rehabilitation | 33, 225 |
| abstract_inverted_index.shoulder/elbow | 180 |
| abstract_inverted_index.rehabilitation, | 239 |
| abstract_inverted_index.rehabilitation. | 16, 98, 250, 265 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 94 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 13 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.4399999976158142 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.99115487 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |